About

Ning Wang is a professor of bioengineering and the director of the Institute for Mechanobiology at Northeastern University College of Engineering. His research focuses on mechanobiology and mechanomedicine, investigating mechanomemory in cancer and stem cell systems. Professor Wang employs advanced research techniques to understand cell mechanics, including cytoskeletal biomechanics, control of cell form and function, bio-imaging of cytoskeletal structures, mechanotransduction, nuclear deformation, gene expression, and mechanical biotechnologies applied to cells, tissues, and organisms. He developed intracellular stress tomography to study stress propagation and distribution in living cells and created three-dimensional magnetic twisting cytometry technology to quantify mechanical anisotropy in cells. His groundbreaking work demonstrated that transmembrane adhesion molecules, specifically integrins, mediate force transmission across the cell surface to the cytoskeleton, a fundamental discovery that opened new avenues in cell mechanics research. Additionally, he showed that cytoskeleton tension influences cell shear stiffness and shape stability, and that localized forces can cause cytoplasmic and nuclear deformation in remote cell regions. Recent collaborative research with Harvard and Boston University colleagues revealed that cells can modulate their internal responses to external forces based on the speed of force application, advancing understanding of cellular mechanosensitivity.

Research topics

• Computer Science
• Machine Learning
• Mathematics
• Geology
• Meteorology
• Physics
• Oceanography
• Algorithm
• Mathematical analysis
• Acoustics
• Statistics

Selected publications

• Explainable and probabilistic nearshore wave modeling along living shorelines using a CASGP-X framework

  Ocean Engineering · 2026-03-03

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• Predictions of saltwater intrusion in the Changjiang Estuary: Integrating Machine learning methods with FVCOM

  Journal of Hydrology · 2025-01-30 · 15 citations

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• Improving significant wave height prediction using Chronos model

  Ocean Engineering · 2025-08-28

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• Explainable data-driven modeling of suspended sediment concentration at a deltaic marsh boundary under river regulation and storm events

  Coastal Engineering · 2025-02-05 · 7 citations

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• Graph oscillators: Physics-guided graph modeling of mass–spring–damper systems for trajectory prediction and damage localization

  Mechanical Systems and Signal Processing · 2024-03-01 · 8 citations

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• Data-driven modeling of Bay-Ocean wave spectra at bridge-tunnel crossing of Chesapeake Bay, USA

  Applied Ocean Research · 2023-03-22 · 6 citations

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• PHYSICS-INFORMED DEEP LEARNING OF NEARSHORE WAVE PROCESSES

  Coastal Engineering Proceedings · 2023-09-01 · 1 citations

  articleOpen access

  The paper introduces the NWnets, a physics-informed deep learning model for reconstructing nearshore wave fields and mapping bathymetry. The physics encoded into the deep neural networks are the wave energy balance equation and dispersion relation. Insights into the model capability are gained through application of the NWnets to a laboratory experiment of wave transformation over a circular shoal. If the bathymetry and discrete measurements of wave height are available, the NWnets model is capable of simulating nearshore wave transformation. Moreover, the extended NWnets can be used for depth inversion if the bathymetry is unknown. Two methods for simultaneously estimating water depths and surface waves are presented. If surface wave number and limited wave height measurements are available from remote sensing platforms, the first method employs wave numbers and scarce measurements of wave height as training data. The second method utilizes scarce wave height and limited water depth measurements as training points to reconstruct bathymetry and wave fields. The results show that both methods are capable of simultaneously mapping the bathymetry and waves when the locations of training points are appropriately distributed.

  PublisherOA PDFDOI

• Monitoring of wave, current, and sediment dynamics along the Chincoteague living shoreline, Virginia

  Antarctica A Keystone in a Changing World · 2023-01-01 · 3 citations

  articleOpen access

  Nature-based features, also called living shorelines, are increasingly applied in coastal protection and restoration. However, the processes and mechanisms (feedbacks and interactions) of wave attenuation, current velocity change, and sediment deposition and erosion along the living shoreline remain unclear, thus limiting the adaptive management of living shoreline restoration projects for coastal shoreline resilience under future storm conditions. In this study, wave, current, and sediment dynamics along the Little Toms Cove living shoreline, Chincoteague National Wildlife Refuge, Virginia, a low wave energy environment, were investigated during a 2-month winter period in 2019 to examine the effects of living shoreline structures on shoreline protection and oyster habitat enhancement. It was found that wave attenuation by the living shoreline structures (oyster castles or constructed oyster reefs) is dependent on water depth, wind speed, wind direction, and local bathymetry. Analysis of observed data indicate that the oyster castles along the Little Toms Cove living shoreline play a limited role in wave attenuation in this low wave energy environment. During the 2-month winter period, wave energy was attenuated by 39.7 percent when oyster castles were emergent or slightly submerged with south-west winds. In contrast, when the oyster castles were fully submerged, wave energy behind the oyster castles increased by 38.6 percent. The construction of oyster castles affected circulation patterns with increase or decrease in velocity at nearshore waters protected by the castles depending on loca-tions of measurements in relation to the oyster castles. Bottom shear stress analysis indicates that tidal currents play a larger role than waves on shoreline and marsh edge erosion along the Little Toms Cove shoreline during the 2 months of field monitoring. The oyster castles protecting the marsh edge and tidal flat from erosion resulted in higher fine sediment concentration in the water column landward of the castles because more sediment was retained in the lee side of the castles. It is important to maximize sediment within the wetlands and adjacent mudflats behind the oyster castles. Erosion from the marsh edge and interior serves as the major source of sediment for this wetland system due to the limited sediment supply in Assateague Channel. Furthermore, it was found that the oyster castles along the Little Toms Cove living shoreline were inundated more than 60 percent of the time, leading to the enhanced oyster habitat as evidenced by suitable velocity (less than 10 centimeters per second) and mean grain size (less than 0.08 millimeters) for oyster feeding and the increased oyster shell density and growth in the intertidal zone protected by the castles than in the control area. More field data (for example, concurrent monitoring of sediment concentration and salinity) over other seasons (for example, summer) could help examine the long term and combined engineering and ecological benefits of living shoreline restoration projects under seasonal and enhanced future storm conditions.

  PublisherOA PDFDOI

• Simultaneous mapping of nearshore bathymetry and waves based on physics-informed deep learning

  Coastal Engineering · 2023-05-15 · 24 citations

  articleOpen accessCorresponding

  This paper uses physics-informed neural networks (PINNs) to simultaneously determine nearshore water depths and wave height fields based on remote sensing of the ocean surface with limited or sparse measurements. Two methods that integrate the knowledge of water wave mechanics and fully connected neural networks are introduced. The first method utilizes observed wave celerity fields and scarce measurements of wave height as training data. The model performance was examined with linear waves over an alongshore varying barred beach and nonlinear waves over an alongshore uniform barred beach. The second method uses scarce wave height and water depth measurements as training points, and the model performance was investigated with water waves over a circular shoal and the alongshore varying barred beach. One advantage of applying PINNs to solve bathymetry inversion problems is that wave height and bathymetry can be simultaneously estimated by PINN models. Thus, the impact of wave amplitude dispersion on depth inversion in nonlinear wave systems can be considered without measuring the entire wave height field. Overall, this study demonstrates the potential of the inverse PINN model as a promising tool for estimating nearshore bathymetry and reconstructing wave fields using observations from different remote sensing platforms.

  PublisherDOI

• Learning dynamics from coarse/noisy data with scalable symbolic regression

  Mechanical Systems and Signal Processing · 2023-01-27 · 13 citations

  articleSenior authorCorresponding
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Frequent coauthors

• Qin Chen

  China Metallurgical Geology Bureau

  13 shared

• Zhao Chen

  Southeast University

  10 shared

• Hongqing Wang

  U.S. Geological Survey, Wetland and Aquatic Research Center

  7 shared

• Ling Zhu

  Wuhan University of Technology

  6 shared

• Gregg A. Snedden

  5 shared

• Lukasz M. Niemoczynski

  5 shared

• William D. Capurso

  5 shared

• Hao Sun

  2 shared

Awards & honors

• PNAS Perspective Defines Mechanomedicine as Target for Disea…